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Content Analysis and Learning Analytics on Interactions of Unsupervised Learners in an Online Learning Environment

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Part of the Learning and Analytics in Intelligent Systems book series (LAIS,volume 7)

Abstract

In line with the smart learning environment, online learners require not only adequate supports to help them overcome loneliness and demotivation, but they also need pertinent guidance and orientation for proper interactions in line with their learning experience. In a socio-constructivism approach, this study examines unsupervised synchronous learner-learner interactions that took place in an online campus of a French university. At a master’s level, three teams of French speaking learners, with different cultural background, were given the same problem based assignment to complete. With the aim of understanding how interactions took place in this educational setup, content analysis was used, based on the Activity Theory of Engeström, on a total of 25 online meetings, organized by three teams, totaling to 3585 number of lines of logged textual interactions. Interestingly, it was found that the teams successfully completely their tasks by exchanging on almost the same types of interactions. However, the quality of these interactions can still be enhanced to generate higher cognitive and metacognitive discussions, as per the expectations of the tutors. Suggestion is given for recommender systems to be integrated in smart learning environment.

Keywords

  • Analytics
  • Unsupervised interactions
  • Learning
  • Content analysis
  • Online learning environment

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Correspondence to Shireen Panchoo .

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Panchoo, S., Jaillet, A. (2020). Content Analysis and Learning Analytics on Interactions of Unsupervised Learners in an Online Learning Environment. In: Serrhini, M., Silva, C., Aljahdali, S. (eds) Innovation in Information Systems and Technologies to Support Learning Research. EMENA-ISTL 2019. Learning and Analytics in Intelligent Systems, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-030-36778-7_6

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